Sensor coordinate calibration in an ultrasound system

ABSTRACT

There is disclosed an embodiment for performing a calibration of a sensor by using an image registration between a three-dimensional ultrasound image and computerized tomography (CT) image. An ultrasound image forming unit includes an ultrasound probe and forms a three-dimensional ultrasound image of a target object. A sensor is coupled to the ultrasound probe. A memory stores a three-dimensional computed tomography (CT) image of the target object and position information on a position between the three-dimensional ultrasound image and the sensor. A processor performs image registration between the three-dimensional CT image and the three-dimensional ultrasound image to form a first transformation function for transforming a position of the sensor to a corresponding position on the three-dimensional CT image and performs calibration of the sensor by applying the position information to the first transformation function.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from Korean Patent ApplicationNo. 10-2009-0070994 filed on Jul. 31, 2009, the entire subject matter ofwhich is incorporated herein by reference.

TECHNICAL FIELD

The present invention generally relates to ultrasound systems, and moreparticularly to an ultrasound system and method for performing sensorcoordinate calibration through image-based registration between athree-dimensional ultrasound image and a computerized tomography (CT)image.

BACKGROUND

The ultrasound system has become an important and popular diagnostictool due to its non-invasive and non-destructive nature. Modernhigh-performance ultrasound imaging diagnostic systems and techniquesare commonly used to produce two or three-dimensional images of internalfeatures of patients (target objects).

However, the ultrasound system suffers from inherent shortcomings of anultrasound image such as a low signal-to-noise ratio and a limited fieldof view. Thus, the image registration of a CT (or MR) image onto theultrasound image has been introduced in order to compensate fordeficiencies of the ultrasound image. A sensor has been used to performthe image registration of a CT (or MR) image onto the ultrasound image.Researches have been introduced to calibrate the sensor to matchcoordinates of the CT image and coordinates of the sensor.

Conventionally, after outer markers are attached on a surface of atarget object, a CT image and an ultrasound image for the target objectwith the markers are acquired. Thereafter, the calibration is carriedout by using a relationship between coordinates of the markers in the CTand ultrasound image. That is, the outer markers should be attached tothe surface of the target objects before obtaining the CT image and theultrasound image and be maintained in the same position until completingthe acquisition of the ultrasound image. Moreover, a sensor must sensethe positions of the respective outer markers.

Further, the registration between the coordinate of the CT image and thecoordinate of the sensor has been performed by manually inputting innermarkers on the CT image. Thus, a user of the ultrasound system had toinput the inner markers, which causes the registration between thecoordinate of the CT image and the coordinate of the sensor to be wrong.

SUMMARY

An embodiment for forming a plurality of three-dimensional ultrasoundimages is disclosed herein. In one embodiment, by way of non-limitingexample, an ultrasound system, comprises: an ultrasound image formingunit including a ultrasound probe and being configured to form athree-dimensional ultrasound image of a target object; a sensor coupledto the ultrasound probe; a memory configured to store athree-dimensional computed tomography (CT) image of the target objectand position information on a position between the three-dimensionalultrasound image and the sensor; and a processor configured to performimage registration between the three-dimensional CT image and thethree-dimensional ultrasound image to thereby form a firsttransformation function for transforming a position of the sensor to acorresponding position on the three-dimensional CT image and performcalibration of the sensor by applying the position information to thefirst transformation function.

In another embodiment, a method of performing a calibration of a sensor,comprises: a) obtaining a three-dimensional ultrasound image of a targetobject obtained by the ultrasound system and a three-dimensional CTimage; b) calculating a position information on a position between thethree-dimensional ultrasound image and the sensor; c) performingregistration between the three-dimensional ultrasound image and thethree-dimensional CT image to obtain a first transformation function fortransforming a position of the sensor to a corresponding position on thethree-dimensional CT image; and d) performing calibration of the sensorby applying the position information to the first transformationfunction.

The Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key or essentialfeatures of the claimed subject matter, nor is it intended to be used indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an illustrative embodiment of anultrasound system.

FIG. 2 is a block diagram showing an illustrative embodiment of anultrasound image forming unit.

FIG. 3 is an illustrative embodiment of an ultrasound probe.

FIG. 4 is a block diagram showing an illustrative embodiment of aprocessor.

FIG. 5 is a schematic diagram showing an example of eigenvalues in theHessian matrix.

DETAILED DESCRIPTION

A detailed description may be provided with reference to theaccompanying drawings. One of ordinary skill in the art may realize thatthe following description is illustrative only and is not in any waylimiting. Other embodiments of the present invention may readily suggestthemselves to such skilled persons having the benefit of thisdisclosure.

FIG. 1 is a block diagram showing an illustrative embodiment of anultrasound system which embodies the methods of the present invention.The ultrasound system 100 may include an ultrasound image forming unit110, a sensor 120, a memory 130, a processor 140 and a display unit 150.

The ultrasound image forming unit 110 may be configured to transmitultrasound signals to a target object (not shown) and receive ultrasoundecho signals reflected from the target object. The ultrasound imageforming unit 110 may be further configured to form a three-dimensionalultrasound image of the target object based on the received ultrasoundecho signals.

FIG. 2 is a block diagram showing an illustrative embodiment of anultrasound image forming unit 110. The ultrasound image forming unit 110may include a transmit (Tx) signal generating section 111, an ultrasoundprobe 112 including a plurality of transducer elements (not shown), abeam former 113, an ultrasound data forming section 114 and an imageforming section 115.

The Tx signal generating section 111 may generate Tx signals accordingto an image mode set in the ultrasound system 100. The image mode mayinclude a brightness (B) mode, a Doppler (D) mode, a color flow mode,etc. In one exemplary embodiment, the B mode may be set in theultrasound system 100 to obtain a B mode ultrasound image.

The ultrasound probe 112 may receive the Tx signals from the Tx signalgenerating section 111 and generate ultrasound signals, which may travelinto the target object. The ultrasound probe 112 may further receiveultrasound echo signals reflected from the target object and convertthem into electrical receive signals. In such a case, the electricalreceive signals may be analog signals. The ultrasound probe 112 may be athree-dimensional probe, a two-dimensional probe, a one-dimensionalprobe or the like.

FIG. 3 is an illustrative embodiment of an ultrasound probe 112. Atleast one transducer element (not shown) of the ultrasound probe 112generates an image plane IP, which is used to scan a region of interestROI. The image plane IP may be one of slice planes of thethree-dimensional ultrasound image. The sensor 120 is attached to thehousing of the ultrasound probe 112 to determine the position andorientation of the image plane IP. The ultrasound system 100 coupledwith the ultrasound probe 112 via the probe cable 105 can use the datagenerated by the sensor 120 to determine the position and orientation ofthe sensor 120 and/or the image plane IP, as described below.

In this preferred embodiment, the sensor 120 is a magnetic sensor thatmonitors the free-hand movement of the ultrasound probe 112 in sixdegrees of freedom with respect to a transducer element 170. As shown inFIG. 3, the sensor 120 and the transducer element 170 each define anorigin (122, 172, respectively) defined by three orthogonal axes (X′,Y′, Z′ and X″, Y″, Z″, respectively). The sensor 120 monitors thetranslation of the origin 122 with respect to the origin 172 of thetransducer element 170 to determine position and monitor the rotation ofthe X′, Y′, Z′ axes with respect to the X″, Y″, Z″ axes of thetransducer element 170 to determine orientation.

The position and orientation of the sensor 120 can be used to determinethe position and orientation of the image plane IP. As shown in FIG. 3,the image plane IP defines an origin OR defined by three orthogonal axesX, Y, Z, which are preferably aligned with the origin of a centeracoustic line generated by the ultrasound probe 112. The position of theorigin 122 and the orientation of axes X′, Y′, Z′ of the sensor 120 maynot precisely coincide with the position of the origin OR and theorientation of the axes X, Y, Z of the image plane IP. For example, inFIG. 3, the origin OR of the image plane IP is offset from the origin122 of the sensor 120 by a distance z₀ along the Z-direction and adistance of y₀ along the Y-direction. In FIG. 3, there is no offsetalong the X-direction nor is there a rotational offset in theorientation of the axes. Accordingly, the position and orientation ofthe sensor 120 do not directly describe the position and orientation ofthe image plane IP.

To determine the position an orientation of the image plane IP from theposition and orientation of the sensor 120, sensor calibration data isused to transform the position and orientation of the sensor 120 to theposition and orientation of the image plane IP. For simplicity, the term“position and orientation” is used to broadly refer to position and/ororientation. Accordingly, if the sensor 120 has the same orientation asthe image plane IP, then the position and orientation calibration datamay not contain any orientation calibration data. Similarly, as shown inFIG. 3, the sensor 120 may not have a positional offset with respect toone or more axes of the image plane IP.

There are a number of ways of defining the image plane/sensor offset.One method of calibrating at least some types of sensors use threeorthogonal linear dimension offsets in X, Y, Z and three rotation anglesabout each of these axes. Other methods include using a positiontransformation matrix or quaternions, which are described in the usermanual for the mini Bird™ and the Flock of Bird™ systems by AscensionTechnology Corp.

As described above, the ultrasound probes with position and orientationsensors are typically used only with ultrasound systems that contain thecalibration data for the probe/sensor pair. Conventionally, theprobe/sensor pair is calibrated, and the calibration data is stored inthe ultrasound system 100, which will be used in conjunction with theprobe/sensor pair. If the probe/sensor pair is to be used with adifferent ultrasound system, then the probe/sensor pair typically needsto be re-calibrated on that different ultrasound system. Sincesonographers are often unable or unwilling to perform probe/sensor paircalibration, probe/sensor pairs are often used only with the ultrasoundsystem for which the probe/sensor pair was initially calibrated.

Referring back to FIG. 2, the beam former 113 may convert the electricalreceive signals outputted from the ultrasound probe 112 into digitalsignals. The beam former 113 may further apply delays to the digitalsignals in consideration of the distances between the transducerelements and focal points to thereby output receive-focused signals.

The ultrasound data forming section 114 may form a plurality ofultrasound data by using the receive-focused signals. In one embodiment,the plurality of ultrasound data may be radio frequency (RF) data or IQdata. The image forming section 115 may form the three-dimensionalultrasound image of the target object based on the ultrasound data.

Referring back to FIG. 1, the sensor 120 may be mounted on one side ofthe ultrasound probe 112. In one embodiment, by way of non-limitingexamples, the sensor 120 may be built in the ultrasound probe 112 to beaway from the plurality of transducer elements (not shown) by apredetermined distance. Alternatively, the sensor 120 may be externallymounted on the ultrasound probe 112 to be away from the plurality oftransducer elements. The sensor 120 may include three-dimensionalsensor, which can detect a three-dimensional position and an angle ofthe ultrasound probe 112.

The memory 130 may store a three-dimensional CT image of the targetobject. In one embodiment, by way of non-limiting examples, thethree-dimensional CT image may be a three-dimensional CT image of aliver in which a diaphragm and a blood vessel are extracted. The memory130 may store information on a position between the three-dimensionalultrasound image and the sensor 120 (hereinafter, referred to as“position information”). The position information may includeinformation on a distance between the transducer elements (not shown)and the sensor 120. In one embodiment, by way of non-limiting examples,the memory 120 may include at least one of a random access memory (RAM),a hard disk drive or the like.

The processor 140 may be configured to perform registration between thethree-dimensional CT image and the three-dimensional ultrasound image,thereby forming a transformation function (T for representing the probe)ultrasound probe 112 on the three-dimensional CT image. Furthermore, theprocessor 140 may perform calibration of the sensor 120 to matchcoordinates of the three-dimensional CT image (not shown) andcoordinates of the sensor 120 based on the position information and thetransformation function.

FIG. 4 is a block diagram showing an illustrative embodiment of theprocessor 140. The processor 140 may include a diaphragm extractingsection 141, a vessel extracting section 142, a diaphragm refiningsection 143, a registration section 144, a calibration section 145 andan image processing section 146.

The diaphragm extracting section 141 may be configured to extract adiaphragm from the three-dimensional ultrasound image formed in theultrasound image forming unit 110. In one embodiment, the diaphragmextracting section 141 may perform a Hessian matrix based flatness testupon the three-dimensional ultrasound image to extract the diaphragm.The diaphragm may be considered as a curved surface in thethree-dimensional ultrasound image. Thus, regions in which a voxelintensity change in a normal direction at a surface is greater than avoxel intensity change in a horizontal direction at the surface may beextracted as the diaphragm. FIG. 5 is a schematic diagram showing anexample of eigenvalues λ₁, λ₂ and λ₃ in the Hessian matrix.

Hereinafter, an operation of the diaphragm extracting section 141 willbe described in detail. The diaphragm extracting section 141 may beconfigured to select voxels having a relatively high flatness value. Theflatness μ(v) may be defined as the following equation (1).

μ(v)=φ₁(v)φ₂(v)φ₃(v)/φ₃ _(max) (v)  (1)

wherein φ₁(v), φ₂(v) and φ₃(v) in the equation (1) may be represented asthe following equation (2).

$\begin{matrix}{{{\varphi_{1}(v)} = \left( {1 - \frac{\lambda_{1}(v)}{\lambda_{3}(v)}} \right)^{2}},{{\varphi_{2}(v)} = \left( {1 - \frac{\lambda_{2}(v)}{\lambda_{3}(v)}} \right)^{2}},{{\varphi_{3}(v)} = {\sum\limits_{i}{\lambda_{i}(v)}^{2}}}} & (2)\end{matrix}$

wherein λ₁, λ₂ and λ₃ denote eigenvalues of the Hessian matrix at voxelv. The flatness μ(v) may be normalized to have values ranging from 0-1.A flatness map may be formed based on the flatness values obtained fromall of the voxels according to the equations (1) and (2). Thereafter,the voxels having a relatively high flatness value are selected. In oneembodiment, the voxels having the flatness over 0.1 may be selected.

The diaphragm extracting section 141 may be further configured toperform the morphological opening upon the selected voxels to removesmall clutters therefrom. The morphological opening may be carried outby sequentially performing erosion and dilation. That is, apredetermined number of the voxels are removed in the edges of the areain which the voxels exist, and thus, the area becomes contracted(erosion). In this manner, it becomes possible to remove small clutters.Thereafter, the edges of the area are expanded by the predeterminednumber of the voxels (dilation). These erosion and dilation may beperformed by one or more voxels.

The diaphragm is the largest surface in the three-dimensional ultrasoundimage. The largest surface may be selected among candidate surfacesobtained by the intensity-based connected component analysis (CCA) forthe voxels and the selected surface may be regarded as the diaphragm inthe three-dimensional ultrasound image. The voxel-based CCA is one ofthe methods of grouping regions in which voxel values exist. Forexample, the number of voxels connected to each of the voxels through aconnectivity test by referring to values of voxels neighboring thecorresponding voxel (e.g., 26 voxels) may be computed. The voxels, ofwhich connected voxels are greater than the predetermined number, areselected as candidate groups. Since the diaphragm is the widest curvedsurface in the ROI, the candidate group having the most connected voxelsmay be selected as the diaphragm. The surface of the diaphragm may besmoothened.

The vessel extracting section 142 may be configured to perform vesselextraction upon the three-dimensional ultrasound image. The vesselextracting section 142 may be configured to perform the vesselextraction through ROI masking, vessel segmentation and classification.

To avoid mis-extraction of the vessels due to mirroring artifacts, theROI masking may be applied to the three-dimensional ultrasound image bymodeling the diaphragm as a polynomial curved surface. For example, theROI masking may be used to model the diaphragm as the polynomial curvedsurface by using the least means square. However, if all of the lowerportions of the modeled polynomial curved surface are eliminated, thenmeaningful vessel information may be lost at a portion of regions due toan error of the polynomial curved surface. To avoid losing the vesselinformation, the lower portion of the modeled polynomial curved surfacemay be eliminated with a marginal distance. For example, the marginaldistance may be set to about 10 voxels at a lower portion of the ROImask.

Subsequently, the vessel extracting section 142 may be furtherconfigured to segment a vessel region and a non-vessel region. Toexclude non-vessel high intensity regions such as the diaphragm andvessel walls, a low intensity bound value having a less reference boundvalue in the ROI masked three-dimensional ultrasound image may be set asa reference bound value. Thereafter, voxels with a higher intensityvalue than the reference bound value may be removed. The remainingregions may be binarized by using an adaptive threshold value. Then, thebinarized segments may be labeled as vessel candidates.

Next, the vessel extracting section 142 may be further configured toremove non-vessel-type clutters from the binarization image to form realvessel regions from the vessel candidates. In one embodiment, the vesselclassification may include a size test, which evaluates the quality offit to a cylindrical tube, for filtering out tiny background clutters, astructure-based vessel test for removing non-vessel type clutters, i.e.,an initial vessel test, a gradient magnitude analysis, and a finalvessel test for precisely removing the clutters. Although some cluttersare not removed through the structure-based vessel test, an initialthreshold may be marginally set so that all vessels may be included. Forexample, a threshold value of the initial vessel test may be set to 0.6.At the final vessel test, clutters, which may be formed by small shadingartifacts having low gradient magnitudes, may be precisely removed byconsidering variation of voxel values, i.e., gradient magnitudes, tothereby extract vessel data. In one embodiment, a threshold of the finalvessel test may be set to 0.4.

The diaphragm refining section 143 may be configured to refine thediaphragm region by removing the clutters with the extracted vesselregions. The clutters are mainly placed near the vessel walls. Forexample, the vessel walls of inferior vena cava (IVC) are more likely tobe connected to the diaphragm and cause clutters. These clutters maydegrade the accuracy of the feature based registration, and thus, it maybe necessary to refine the diaphragm region. To refine the diaphragm,the vessel regions are extracted according to the vessel extractionmentioned above, the extracted vessel regions may be dilated, and thenthe dilated vessel regions may be subtracted from the initiallyextracted diaphragm region to estimate vessel walls. The estimatedvessel walls may be removed from the diaphragm region. Finally, thediaphragm region may be extracted by applying CCA and the size test.

The registration section 144 may be configured to perform the imageregistration between the three-dimensional ultrasound and CT image. Theregistration section 144 may extract sample points from the vesselregions and the diaphragm region, respectively, among the featuresextracted from the three-dimensional ultrasound image. Also, after thevessel regions and the diaphragm region are extracted from the CT image,the registration section 144 may extract sample points from the vesseland the diaphragm region, respectively. The image registration betweenthe three-dimensional ultrasound and CT image may be performed based onthe extracted sample points to thereby form the transformation function(T_(probe)) between the three-dimensional CT image and thethree-dimensional ultrasound image. The transformation function(T_(probe))) may be given by a matrix robe. and used to transform aposition of the ultrasound probe 112 to a corresponding position on thethree-dimensional CT image.

The calibration section 145 may perform the calibration of the sensor120 based on the transformation matrix (T_(probe)) from the registrationsection 144 and the position information stored in the memory 130. Moreparticularly, the calibration section 145 may form a transformationmatrix (T_(sensor)) between the sensor 120 and the three-dimensionalultrasound image, i.e., a transformation matrix representing a positionof the sensor 120 with respect to the three dimensional ultrasoundimage. The transformation matrix (T_(sensor)) may be given by a matrix.The transformation matrix (T_(sensor)) may be defined as the followingequation (3).

$\begin{matrix}{{T_{sensor} = {\begin{matrix}{r\; 11} & {r\; 12} & {r\; 13} & x \\{r\; 21} & {r\; 22} & {r\; 23} & y \\{r\; 31} & {r\; 32} & {r\; 33} & z \\0 & 0 & 0 & 1\end{matrix}}}{{r\; 11} = {{\cos \; \theta \; y*\cos \; \theta \; z} + {\sin \; \theta \; x*\sin \; \theta \; y*\sin \; \theta \; z}}}{{r\; 12} = {{\sin \; \theta \; z*\cos \; \theta \; y} - {\sin \; \theta \; x*\sin \; \theta \; y*\cos \; \theta \; z}}}{{{r\; 13} = {\cos \; \theta \; x*\sin \; \theta \; y}},{{r\; 21} = {\sin \; \theta \; z*\cos \; \theta \; x}}}{{{r\; 22} = {\cos \; \theta \; z*\sin \; \theta \; x}},{{r\; 23} = {\sin \; \theta \; x}}}{{r\; 31} = {{\sin \; \theta \; z*\sin \; \theta \; x*\cos \; \theta \; y} - {\cos \; \theta \; z*\sin \; \theta \; y}}}{{r\; 32} = {{{- \cos}\; \theta \; z*\sin \; \theta \; x*\cos \; \theta \; y} - {\sin \; \theta \; z*\sin \; \theta \; y}}}{{r\; 33} = {\cos \; \theta \; x*\cos \; \theta \; y}}} & (3)\end{matrix}$

wherein, x denotes coordinate of a lateral direction of the sensor 120,y denotes coordinate of an elevation direction of the sensor 120, zdenotes an axial direction of the sensor 120, θ_(x) denotes an angle ofthe sensor 120 from the x-axis, θ_(y) denotes an angle of the sensor 120from the y-axis, and θ_(z) denotes an angle of the sensor 120 from thez-axis. The elevation direction may be a swing direction of thetransducer elements, the axial direction may be a scan line directionfrom the transducer elements and the lateral direction may be alongitudinal direction of the transducer elements.

The calibration section 145 may perform the calibration based on thetransformation matrix (T_(probe)) and the transformation matrix(T_(sensor)). The robe, calibration section 145 may form atransformation matrix (T) representing the position of the sensor 120 onthe three-dimensional CT image. In one embodiment, the calibrationsection 145 may form the transformation matrix (T) through matrixmultiplication of the transformation matrix (T_(probe)) and thetransformation matrix (T_(sensor)).

The image processing section 146 may apply the transformation matrix (T)to the three-dimensional CT image to thereby form a two-dimensional CTimage according to a two-dimensional ultrasound image.

Referring back to FIG. 1, the display unit 150 may display thetwo-dimensional CT image, which is provided from the processor 140.Furthermore, the display unit 150 may display the three-dimensionalultrasound image and the three-dimensional CT image.

Any reference in this specification to “one embodiment,” “anembodiment,” “example embodiment,” “illustrative embodiment,” etc. meansthat a particular feature, structure or characteristic described inconnection with the embodiment is included in at least one embodiment ofthe present invention. The appearances of such phrases in various placesin the specification are not necessarily all referring to the sameembodiment. Further, when a particular feature, structure orcharacteristic is described in connection with any embodiment, it issubmitted that it is within the purview of one skilled in the art toaffect such feature, structure or characteristic in connection withother ones of the embodiments.

Although embodiments have been described with reference to a number ofillustrative embodiments thereof, it should be understood that numerousother modifications and embodiments can be devised by those skilled inthe art that will fall within the spirit and scope of the principles ofthis disclosure. More particularly, numerous variations andmodifications are possible in the component parts and/or arrangements ofthe subject combination arrangement within the scope of the disclosure,the drawings and the appended claims. In addition to variations andmodifications in the component parts and/or arrangements, alternativeuses will also be apparent to those skilled in the art.

1. An ultrasound system, comprising: an ultrasound image forming unitincluding an ultrasound probe and being configured to form athree-dimensional ultrasound image of a target object; a sensor coupledto the ultrasound probe; a memory configured to store athree-dimensional computed tomography (CT) image of the target objectand position information on a position between the three-dimensionalultrasound image and the sensor; and a processor configured to performimage registration between the three-dimensional CT image and thethree-dimensional ultrasound image to thereby form a firsttransformation function for transforming a position of the sensor to acorresponding position on the three-dimensional CT image and performcalibration of the sensor by applying the position information to thefirst transformation function.
 2. The ultrasound system of claim 1,wherein the target object includes a liver.
 3. The ultrasound system ofclaim 1, wherein the processor comprises: a diaphragm extracting sectionconfigured to extract a diaphragm region from candidate surfacesobtained by using an intensity-based connected component analysis (CCA);a vessel extracting section configured to extract vessel regions byremoving non-vessel-type clutters from vessel candidates obtained byremoving voxels of the three-dimensional ultrasound image havingintensity value greater than a reference bound value; a registrationsection configured to extract sample points from the diaphragm regionand the vessel regions and perform the image registration between thethree-dimensional ultrasound and CT image based on the extracted samplepoints to thereby form the first transformation function; and acalibration section configured to perform calibration of the sensor tomatch coordinates of the CT image and coordinates of the sensor based onthe first transformation function and the position information.
 4. Theultrasound system of claim 3, wherein the diaphragm extracting sectionis further configured to: obtain flatness values from voxels of thethree-dimensional ultrasound image; select voxels having a flatnessvalue greater than a reference value; remove a predetermined number ofthe voxels in edges of an area in which voxel values exist; expandingthe edges by the predetermined number of the voxels; obtain thecandidate surfaces from the voxels by the CCA; and select a widestsurface among the candidate surfaces to extract the diaphragm region. 5.The ultrasound system of claim 3, wherein the vessel extracting sectionis further configured to: model the diaphragm region to a polynomialcurved surface in the three-dimensional ultrasound image to form aregion of interest (ROI) mask; perform ROI masking by applying the ROImask to the three-dimensional ultrasound image; form the vesselcandidates by removing voxels having the intensity value greater thanthe reference bound value from the ROI masked three-dimensionalultrasound image; and extract the vessel regions by sequentiallyperforming a size test, a structure-based vessel test, a gradientmagnitude analysis, and a final vessel test for removing thenon-vessel-type clutters from the vessel candidates
 6. The ultrasoundsystem of claim 3, wherein the calibration section is further configuredto: form a second transformation function between the three-dimensionalultrasound image and the sensor based on the position information;multiply the first transformation function by the second transformationfunction to form a third transformation function for representing theposition of the sensor on the three-dimensional CT image; and performcalibration of the sensor based on the third transformation function. 7.A method of calibrating an ultrasound system having an ultrasound probeand a sensor, comprising: a) obtaining a three-dimensional ultrasoundimage of a target object and a three-dimensional CT image of the targetobject; b) calculating a position information on a position between thethree-dimensional ultrasound image and the sensor; c) performingregistration between the three-dimensional ultrasound image and thethree-dimensional CT image to obtain a first transformation function fortransforming a position of the sensor to a corresponding position on thethree-dimensional CT image; and d) performing calibration of the sensorby applying the position information to the first transformationfunction.
 8. The method of claim 7, wherein the target object is aliver.
 9. The method of claim 7, wherein the step c) comprises: c1)extracting a diaphragm region from candidate surfaces obtained by usingan intensity-based connected component analysis (CCA); c2) extractingvessel regions by removing non-vessel-type clutters from vesselcandidates obtained by removing voxels of the three-dimensionalultrasound image having intensity value greater than a reference boundvalue; c3) extracting sample points from the diaphragm region and thevessel regions; and c4) performing the image registration between thethree-dimensional ultrasound and CT image based on the extracted samplepoints to form the first transformation function.
 10. The method ofclaim 9, wherein the step c1) comprises: obtaining flatness from voxelsof the three-dimensional ultrasound image; selecting voxels having aflatness greater than a reference value; removing a predetermined numberof the voxels in edges of an area in which voxel values exist, expandingthe edges by the predetermined number of the voxels; obtaining thecandidate surfaces from the voxels by the intensity-based connectedcomponent analysis (CCA); and selecting a widest surface among thecandidate surfaces to extract the diaphragm region.
 11. The method ofclaim 9, wherein the step c2) comprises: modeling the diaphragm regionto a polynomial curved surface in the three-dimensional ultrasound imageto form a region of interest (ROI) mask; performing ROI masking byapplying the ROI mask to the three-dimensional ultrasound image; formingvessel candidates by removing voxels having an intensity value greaterthan a reference bound value from the ROI masked three-dimensionalultrasound image; and extracting the vessel regions by sequentiallyperforming a size test, a structure-based vessel test, a gradientmagnitude analysis, and a final vessel test for removing thenon-vessel-type clutters from the vessel candidates.
 12. The method ofclaim 7, wherein the step d) comprises: d1) forming a secondtransformation function between the three-dimensional ultrasound imageand the sensor based on the position information; d2) multiplying thefirst transformation function by the second transformation function toform a third transformation function for representing the position ofthe sensor on the three-dimensional CT image; and d3) performingcalibration of the sensor based on the third transformation function.